Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations16383
Missing cells103
Missing cells (%)< 0.1%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory2.3 MiB
Average record size in memory149.0 B

Variable types

Numeric5
Text9
Categorical4
Boolean5

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
acbal is highly overall correlated with lcyacbalHigh correlation
branch is highly overall correlated with ccy and 3 other fieldsHigh correlation
ccy is highly overall correlated with branch and 3 other fieldsHigh correlation
ibservice is highly overall correlated with branchHigh correlation
lcyacbal is highly overall correlated with acbalHigh correlation
national is highly overall correlated with branch and 3 other fieldsHigh correlation
resident is highly overall correlated with ccy and 2 other fieldsHigh correlation
sector is highly overall correlated with branch and 3 other fieldsHigh correlation
national is highly imbalanced (99.1%) Imbalance
resident is highly imbalanced (99.0%) Imbalance
sector is highly imbalanced (90.9%) Imbalance
ccy is highly imbalanced (98.1%) Imbalance
ibservice is highly imbalanced (62.9%) Imbalance
kyc is highly imbalanced (62.3%) Imbalance
acbal is highly skewed (γ1 = -101.6301297) Skewed
lcyacbal is highly skewed (γ1 = -74.45659072) Skewed
acbal has 732 (4.5%) zeros Zeros
lcyacbal has 746 (4.6%) zeros Zeros

Reproduction

Analysis started2025-01-21 06:34:08.688093
Analysis finished2025-01-21 06:34:10.723866
Duration2.04 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

custid
Real number (ℝ)

Distinct15482
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283508.88
Minimum44
Maximum999998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:10.762075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile8541.5
Q163772.5
median242074
Q3466476.5
95-th percentile683128.9
Maximum999998
Range999954
Interquartile range (IQR)402704

Descriptive statistics

Standard deviation224544.99
Coefficient of variation (CV)0.79202102
Kurtosis-1.1815412
Mean283508.88
Median Absolute Deviation (MAD)192714
Skewness0.4290238
Sum4.6447259 × 109
Variance5.0420452 × 1010
MonotonicityNot monotonic
2025-01-21T12:19:10.815068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
284 41
 
0.3%
1141 14
 
0.1%
300 13
 
0.1%
580 12
 
0.1%
9161 11
 
0.1%
999998 10
 
0.1%
9160 9
 
0.1%
286 8
 
< 0.1%
297 8
 
< 0.1%
362720 7
 
< 0.1%
Other values (15472) 16250
99.2%
ValueCountFrequency (%)
44 2
 
< 0.1%
47 2
 
< 0.1%
49 2
 
< 0.1%
54 2
 
< 0.1%
55 2
 
< 0.1%
56 2
 
< 0.1%
60 2
 
< 0.1%
64 2
 
< 0.1%
65 1
 
< 0.1%
284 41
0.3%
ValueCountFrequency (%)
999998 10
0.1%
772033 1
 
< 0.1%
758020 1
 
< 0.1%
747661 1
 
< 0.1%
747631 1
 
< 0.1%
747584 1
 
< 0.1%
747570 1
 
< 0.1%
747558 1
 
< 0.1%
747526 1
 
< 0.1%
747525 1
 
< 0.1%

name
Text

Distinct12988
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:11.004532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length25
Mean length16.736983
Min length3

Characters and Unicode

Total characters274202
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10923 ?
Unique (%)66.7%

Sample

1st rowDEEPAK DHAMI
2nd rowUPENDRA P. SUBEDI
3rd rowSIDDHI BHANDARI
4th rowSHOBHA SHRESTHA
5th rowAPURVA RAUNIYAR
ValueCountFrequency (%)
bahadur 2533
 
5.8%
thapa 1944
 
4.4%
maya 1481
 
3.4%
shrestha 1431
 
3.3%
gurung 1105
 
2.5%
adhikari 594
 
1.4%
kumari 581
 
1.3%
kumar 580
 
1.3%
ram 548
 
1.3%
magar 539
 
1.2%
Other values (4609) 32393
74.1%
2025-01-21T12:19:11.183511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 56081
20.5%
27520
 
10.0%
R 21700
 
7.9%
H 20428
 
7.4%
I 16810
 
6.1%
N 13380
 
4.9%
S 12764
 
4.7%
U 12614
 
4.6%
M 11008
 
4.0%
T 10701
 
3.9%
Other values (31) 71196
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 274202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 56081
20.5%
27520
 
10.0%
R 21700
 
7.9%
H 20428
 
7.4%
I 16810
 
6.1%
N 13380
 
4.9%
S 12764
 
4.7%
U 12614
 
4.6%
M 11008
 
4.0%
T 10701
 
3.9%
Other values (31) 71196
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 274202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 56081
20.5%
27520
 
10.0%
R 21700
 
7.9%
H 20428
 
7.4%
I 16810
 
6.1%
N 13380
 
4.9%
S 12764
 
4.7%
U 12614
 
4.6%
M 11008
 
4.0%
T 10701
 
3.9%
Other values (31) 71196
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 274202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 56081
20.5%
27520
 
10.0%
R 21700
 
7.9%
H 20428
 
7.4%
I 16810
 
6.1%
N 13380
 
4.9%
S 12764
 
4.7%
U 12614
 
4.6%
M 11008
 
4.0%
T 10701
 
3.9%
Other values (31) 71196
26.0%

national
Categorical

High correlation  Imbalance 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
NP
16339 
US
 
14
IN
 
9
B
 
3
SA
 
3
Other values (10)
 
15

Length

Max length2
Median length2
Mean length1.999939
Min length1

Characters and Unicode

Total characters32765
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowNP
2nd rowNP
3rd rowNP
4th rowNP
5th rowNP

Common Values

ValueCountFrequency (%)
NP 16339
99.7%
US 14
 
0.1%
IN 9
 
0.1%
B 3
 
< 0.1%
SA 3
 
< 0.1%
D 3
 
< 0.1%
T 3
 
< 0.1%
TA 2
 
< 0.1%
AT 1
 
< 0.1%
AU 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Length

2025-01-21T12:19:11.232516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
np 16339
99.7%
us 14
 
0.1%
in 9
 
0.1%
b 3
 
< 0.1%
sa 3
 
< 0.1%
d 3
 
< 0.1%
t 3
 
< 0.1%
ta 2
 
< 0.1%
at 1
 
< 0.1%
au 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 16348
49.9%
P 16339
49.9%
S 18
 
0.1%
U 15
 
< 0.1%
I 10
 
< 0.1%
10
 
< 0.1%
A 7
 
< 0.1%
T 6
 
< 0.1%
D 4
 
< 0.1%
B 3
 
< 0.1%
Other values (4) 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16348
49.9%
P 16339
49.9%
S 18
 
0.1%
U 15
 
< 0.1%
I 10
 
< 0.1%
10
 
< 0.1%
A 7
 
< 0.1%
T 6
 
< 0.1%
D 4
 
< 0.1%
B 3
 
< 0.1%
Other values (4) 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16348
49.9%
P 16339
49.9%
S 18
 
0.1%
U 15
 
< 0.1%
I 10
 
< 0.1%
10
 
< 0.1%
A 7
 
< 0.1%
T 6
 
< 0.1%
D 4
 
< 0.1%
B 3
 
< 0.1%
Other values (4) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16348
49.9%
P 16339
49.9%
S 18
 
0.1%
U 15
 
< 0.1%
I 10
 
< 0.1%
10
 
< 0.1%
A 7
 
< 0.1%
T 6
 
< 0.1%
D 4
 
< 0.1%
B 3
 
< 0.1%
Other values (4) 5
 
< 0.1%

resident
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
NP
16341 
US
 
28
IN
 
5
SA
 
3
NO
 
3
Other values (3)
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters32766
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowNP
2nd rowNP
3rd rowNP
4th rowNP
5th rowNP

Common Values

ValueCountFrequency (%)
NP 16341
99.7%
US 28
 
0.2%
IN 5
 
< 0.1%
SA 3
 
< 0.1%
NO 3
 
< 0.1%
AT 1
 
< 0.1%
AU 1
 
< 0.1%
T 1
 
< 0.1%

Length

2025-01-21T12:19:11.271067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T12:19:11.309088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
np 16341
99.7%
us 28
 
0.2%
in 5
 
< 0.1%
sa 3
 
< 0.1%
no 3
 
< 0.1%
at 1
 
< 0.1%
au 1
 
< 0.1%
t 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 16349
49.9%
P 16341
49.9%
S 31
 
0.1%
U 29
 
0.1%
I 5
 
< 0.1%
A 5
 
< 0.1%
O 3
 
< 0.1%
T 2
 
< 0.1%
1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16349
49.9%
P 16341
49.9%
S 31
 
0.1%
U 29
 
0.1%
I 5
 
< 0.1%
A 5
 
< 0.1%
O 3
 
< 0.1%
T 2
 
< 0.1%
1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16349
49.9%
P 16341
49.9%
S 31
 
0.1%
U 29
 
0.1%
I 5
 
< 0.1%
A 5
 
< 0.1%
O 3
 
< 0.1%
T 2
 
< 0.1%
1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16349
49.9%
P 16341
49.9%
S 31
 
0.1%
U 29
 
0.1%
I 5
 
< 0.1%
A 5
 
< 0.1%
O 3
 
< 0.1%
T 2
 
< 0.1%
1
 
< 0.1%
Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:11.366874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9980468
Min length2

Characters and Unicode

Total characters65500
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row9009
2nd row9009
3rd row9009
4th row9009
5th row9009
ValueCountFrequency (%)
9008 14019
85.6%
9009 1656
 
10.1%
9005 166
 
1.0%
4005 111
 
0.7%
9010 62
 
0.4%
1230 54
 
0.3%
4000 43
 
0.3%
2010 42
 
0.3%
4025 25
 
0.2%
2005 24
 
0.1%
Other values (42) 181
 
1.1%
2025-01-21T12:19:11.463498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32610
49.8%
9 17589
26.9%
8 14026
21.4%
5 393
 
0.6%
4 301
 
0.5%
1 268
 
0.4%
2 196
 
0.3%
3 68
 
0.1%
N 16
 
< 0.1%
P 16
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32610
49.8%
9 17589
26.9%
8 14026
21.4%
5 393
 
0.6%
4 301
 
0.5%
1 268
 
0.4%
2 196
 
0.3%
3 68
 
0.1%
N 16
 
< 0.1%
P 16
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32610
49.8%
9 17589
26.9%
8 14026
21.4%
5 393
 
0.6%
4 301
 
0.5%
1 268
 
0.4%
2 196
 
0.3%
3 68
 
0.1%
N 16
 
< 0.1%
P 16
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32610
49.8%
9 17589
26.9%
8 14026
21.4%
5 393
 
0.6%
4 301
 
0.5%
1 268
 
0.4%
2 196
 
0.3%
3 68
 
0.1%
N 16
 
< 0.1%
P 16
 
< 0.1%
Other values (2) 17
 
< 0.1%

sector
Categorical

High correlation  Imbalance 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
1500
15674 
1000
 
130
1100
 
113
1450
 
93
1300
 
90
Other values (18)
 
283

Length

Max length4
Median length4
Mean length3.9998779
Min length2

Characters and Unicode

Total characters65530
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row1500
2nd row1500
3rd row1500
4th row1500
5th row1500

Common Values

ValueCountFrequency (%)
1500 15674
95.7%
1000 130
 
0.8%
1100 113
 
0.7%
1450 93
 
0.6%
1300 90
 
0.5%
9010 56
 
0.3%
3000 42
 
0.3%
1050 35
 
0.2%
1550 35
 
0.2%
1350 28
 
0.2%
Other values (13) 87
 
0.5%

Length

2025-01-21T12:19:11.508496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1500 15674
95.7%
1000 130
 
0.8%
1100 113
 
0.7%
1450 93
 
0.6%
1300 90
 
0.5%
9010 56
 
0.3%
3000 42
 
0.3%
1050 35
 
0.2%
1550 35
 
0.2%
1350 28
 
0.2%
Other values (13) 87
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 32764
50.0%
1 16402
25.0%
5 15944
24.3%
3 160
 
0.2%
4 137
 
0.2%
9 76
 
0.1%
2 43
 
0.1%
8 2
 
< 0.1%
N 1
 
< 0.1%
P 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32764
50.0%
1 16402
25.0%
5 15944
24.3%
3 160
 
0.2%
4 137
 
0.2%
9 76
 
0.1%
2 43
 
0.1%
8 2
 
< 0.1%
N 1
 
< 0.1%
P 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32764
50.0%
1 16402
25.0%
5 15944
24.3%
3 160
 
0.2%
4 137
 
0.2%
9 76
 
0.1%
2 43
 
0.1%
8 2
 
< 0.1%
N 1
 
< 0.1%
P 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32764
50.0%
1 16402
25.0%
5 15944
24.3%
3 160
 
0.2%
4 137
 
0.2%
9 76
 
0.1%
2 43
 
0.1%
8 2
 
< 0.1%
N 1
 
< 0.1%
P 1
 
< 0.1%

branch
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.727278
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:11.543038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q112
median12
Q312
95-th percentile12
Maximum90
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9721245
Coefficient of variation (CV)0.3702826
Kurtosis78.7952
Mean10.727278
Median Absolute Deviation (MAD)0
Skewness2.1603221
Sum175745
Variance15.777773
MonotonicityNot monotonic
2025-01-21T12:19:11.575027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
12 14414
88.0%
1 1953
 
11.9%
90 8
 
< 0.1%
13 4
 
< 0.1%
11 2
 
< 0.1%
15 2
 
< 0.1%
ValueCountFrequency (%)
1 1953
 
11.9%
11 2
 
< 0.1%
12 14414
88.0%
13 4
 
< 0.1%
15 2
 
< 0.1%
90 8
 
< 0.1%
ValueCountFrequency (%)
90 8
 
< 0.1%
15 2
 
< 0.1%
13 4
 
< 0.1%
12 14414
88.0%
11 2
 
< 0.1%
1 1953
 
11.9%

actype
Real number (ℝ)

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.053287
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:11.610038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q122
median22
Q336
95-th percentile46
Maximum99
Range98
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.167484
Coefficient of variation (CV)0.48672401
Kurtosis8.9556915
Mean27.053287
Median Absolute Deviation (MAD)1
Skewness1.9084843
Sum443214
Variance173.38264
MonotonicityNot monotonic
2025-01-21T12:19:11.648566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
22 6523
39.8%
23 2407
 
14.7%
40 1967
 
12.0%
36 1616
 
9.9%
21 1199
 
7.3%
1 871
 
5.3%
46 685
 
4.2%
18 449
 
2.7%
99 191
 
1.2%
45 138
 
0.8%
Other values (11) 337
 
2.1%
ValueCountFrequency (%)
1 871
 
5.3%
12 10
 
0.1%
13 1
 
< 0.1%
18 449
 
2.7%
21 1199
 
7.3%
22 6523
39.8%
23 2407
 
14.7%
24 34
 
0.2%
36 1616
 
9.9%
39 17
 
0.1%
ValueCountFrequency (%)
99 191
 
1.2%
59 115
 
0.7%
58 1
 
< 0.1%
57 37
 
0.2%
54 7
 
< 0.1%
51 77
 
0.5%
49 37
 
0.2%
48 1
 
< 0.1%
46 685
4.2%
45 138
 
0.8%
Distinct16302
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:11.753083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters196596
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16248 ?
Unique (%)99.2%

Sample

1st row012104445004
2nd row011801001998
3rd row019900670380
4th row012100674414
5th row012104442772
ValueCountFrequency (%)
121 785
 
4.6%
011 81
 
0.5%
014500002842 8
 
< 0.1%
014500002843 7
 
< 0.1%
014500002849 6
 
< 0.1%
014500002841 4
 
< 0.1%
014500002840 4
 
< 0.1%
014500002846 3
 
< 0.1%
014500002972 3
 
< 0.1%
019909999984 3
 
< 0.1%
Other values (16294) 16345
94.8%
2025-01-21T12:19:11.895449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 42966
21.9%
0 36853
18.7%
1 30667
15.6%
3 15904
 
8.1%
6 15104
 
7.7%
4 14660
 
7.5%
5 12984
 
6.6%
8 10803
 
5.5%
9 8081
 
4.1%
7 7708
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 42966
21.9%
0 36853
18.7%
1 30667
15.6%
3 15904
 
8.1%
6 15104
 
7.7%
4 14660
 
7.5%
5 12984
 
6.6%
8 10803
 
5.5%
9 8081
 
4.1%
7 7708
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 42966
21.9%
0 36853
18.7%
1 30667
15.6%
3 15904
 
8.1%
6 15104
 
7.7%
4 14660
 
7.5%
5 12984
 
6.6%
8 10803
 
5.5%
9 8081
 
4.1%
7 7708
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 42966
21.9%
0 36853
18.7%
1 30667
15.6%
3 15904
 
8.1%
6 15104
 
7.7%
4 14660
 
7.5%
5 12984
 
6.6%
8 10803
 
5.5%
9 8081
 
4.1%
7 7708
 
3.9%

ccy
Categorical

High correlation  Imbalance 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
NPR
16288 
USD
 
45
INR
 
16
1.2
 
10
EUR
 
7
Other values (6)
 
17

Length

Max length3
Median length3
Mean length2.9998779
Min length1

Characters and Unicode

Total characters49147
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNPR
2nd rowNPR
3rd rowNPR
4th rowNPR
5th rowNPR

Common Values

ValueCountFrequency (%)
NPR 16288
99.4%
USD 45
 
0.3%
INR 16
 
0.1%
1.2 10
 
0.1%
EUR 7
 
< 0.1%
JPY 6
 
< 0.1%
9.9 5
 
< 0.1%
AUD 2
 
< 0.1%
GBP 2
 
< 0.1%
AED 1
 
< 0.1%

Length

2025-01-21T12:19:11.947011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
npr 16288
99.4%
usd 45
 
0.3%
inr 16
 
0.1%
1.2 10
 
0.1%
eur 7
 
< 0.1%
jpy 6
 
< 0.1%
9.9 5
 
< 0.1%
aud 2
 
< 0.1%
gbp 2
 
< 0.1%
aed 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 16311
33.2%
N 16304
33.2%
P 16296
33.2%
U 54
 
0.1%
D 48
 
0.1%
S 45
 
0.1%
I 16
 
< 0.1%
. 15
 
< 0.1%
1 11
 
< 0.1%
2 10
 
< 0.1%
Other values (7) 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 16311
33.2%
N 16304
33.2%
P 16296
33.2%
U 54
 
0.1%
D 48
 
0.1%
S 45
 
0.1%
I 16
 
< 0.1%
. 15
 
< 0.1%
1 11
 
< 0.1%
2 10
 
< 0.1%
Other values (7) 37
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 16311
33.2%
N 16304
33.2%
P 16296
33.2%
U 54
 
0.1%
D 48
 
0.1%
S 45
 
0.1%
I 16
 
< 0.1%
. 15
 
< 0.1%
1 11
 
< 0.1%
2 10
 
< 0.1%
Other values (7) 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 16311
33.2%
N 16304
33.2%
P 16296
33.2%
U 54
 
0.1%
D 48
 
0.1%
S 45
 
0.1%
I 16
 
< 0.1%
. 15
 
< 0.1%
1 11
 
< 0.1%
2 10
 
< 0.1%
Other values (7) 37
 
0.1%
Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:12.011221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9990844
Min length3

Characters and Unicode

Total characters65517
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row6010
2nd row1018
3rd row4004
4th row6010
5th row6010
ValueCountFrequency (%)
6007 3828
23.4%
6022 2405
14.7%
6023 1967
12.0%
6099 1950
11.9%
6019 1604
9.8%
6010 1199
 
7.3%
6024 686
 
4.2%
6001 644
 
3.9%
1018 449
 
2.7%
1013 278
 
1.7%
Other values (43) 1373
 
8.4%
2025-01-21T12:19:12.113431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23022
35.1%
6 14738
22.5%
2 7707
 
11.8%
1 6213
 
9.5%
9 5511
 
8.4%
7 3846
 
5.9%
3 2527
 
3.9%
4 1074
 
1.6%
8 473
 
0.7%
5 360
 
0.5%
Other values (4) 46
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65517
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23022
35.1%
6 14738
22.5%
2 7707
 
11.8%
1 6213
 
9.5%
9 5511
 
8.4%
7 3846
 
5.9%
3 2527
 
3.9%
4 1074
 
1.6%
8 473
 
0.7%
5 360
 
0.5%
Other values (4) 46
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65517
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23022
35.1%
6 14738
22.5%
2 7707
 
11.8%
1 6213
 
9.5%
9 5511
 
8.4%
7 3846
 
5.9%
3 2527
 
3.9%
4 1074
 
1.6%
8 473
 
0.7%
5 360
 
0.5%
Other values (4) 46
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65517
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23022
35.1%
6 14738
22.5%
2 7707
 
11.8%
1 6213
 
9.5%
9 5511
 
8.4%
7 3846
 
5.9%
3 2527
 
3.9%
4 1074
 
1.6%
8 473
 
0.7%
5 360
 
0.5%
Other values (4) 46
 
0.1%

acbal
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct14536
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-123301.22
Minimum-2.4845915 × 109
Maximum6.8659289 × 108
Zeros732
Zeros (%)4.5%
Negative1282
Negative (%)7.8%
Memory size128.1 KiB
2025-01-21T12:19:12.156880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.4845915 × 109
5-th percentile-25370.739
Q1159.99
median1146.14
Q311029
95-th percentile186563.21
Maximum6.8659289 × 108
Range3.1711844 × 109
Interquartile range (IQR)10869.01

Descriptive statistics

Standard deviation20782781
Coefficient of variation (CV)-168.55291
Kurtosis12560.616
Mean-123301.22
Median Absolute Deviation (MAD)1146.14
Skewness-101.63013
Sum-2.020044 × 109
Variance4.3192398 × 1014
MonotonicityNot monotonic
2025-01-21T12:19:12.205962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 732
 
4.5%
7500 92
 
0.6%
10.95 82
 
0.5%
6.89 47
 
0.3%
5.51 43
 
0.3%
111.34 28
 
0.2%
52761.93 23
 
0.1%
556.85 21
 
0.1%
16.48 20
 
0.1%
100 20
 
0.1%
Other values (14526) 15275
93.2%
ValueCountFrequency (%)
-2484591470 1
< 0.1%
-298976044 1
< 0.1%
-153486495 1
< 0.1%
-140755542.2 1
< 0.1%
-126150923.1 1
< 0.1%
-105162699.3 1
< 0.1%
-88809883.19 1
< 0.1%
-72140776.43 1
< 0.1%
-71093375.91 1
< 0.1%
-66022133.42 1
< 0.1%
ValueCountFrequency (%)
686592892.6 1
< 0.1%
473256847.6 1
< 0.1%
50781069.48 1
< 0.1%
50000763.53 1
< 0.1%
47113884.23 1
< 0.1%
46900376 1
< 0.1%
43494059.78 1
< 0.1%
33598138.54 1
< 0.1%
26700938.59 1
< 0.1%
21445443.53 1
< 0.1%

lcyacbal
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct14546
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-326524.51
Minimum-2.4845915 × 109
Maximum6.8659289 × 108
Zeros746
Zeros (%)4.6%
Negative1280
Negative (%)7.8%
Memory size128.1 KiB
2025-01-21T12:19:12.252398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.4845915 × 109
5-th percentile-26104.207
Q1159.035
median1144.2
Q311050.005
95-th percentile192535.97
Maximum6.8659289 × 108
Range3.1711844 × 109
Interquartile range (IQR)10890.97

Descriptive statistics

Standard deviation24303663
Coefficient of variation (CV)-74.43136
Kurtosis7209.7954
Mean-326524.51
Median Absolute Deviation (MAD)1144.2
Skewness-74.456591
Sum-5.3494511 × 109
Variance5.9066806 × 1014
MonotonicityNot monotonic
2025-01-21T12:19:12.300176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 746
 
4.6%
7500 93
 
0.6%
10.95 82
 
0.5%
6.89 47
 
0.3%
5.51 43
 
0.3%
111.34 28
 
0.2%
52761.93 23
 
0.1%
556.85 21
 
0.1%
16.48 20
 
0.1%
100 20
 
0.1%
Other values (14536) 15260
93.1%
ValueCountFrequency (%)
-2484591470 1
< 0.1%
-1249626339 1
< 0.1%
-747305056.3 1
< 0.1%
-497626940.2 1
< 0.1%
-413825369.5 1
< 0.1%
-298976044 1
< 0.1%
-154492209.2 1
< 0.1%
-152361554.9 1
< 0.1%
-150943204.9 1
< 0.1%
-140755542.2 1
< 0.1%
ValueCountFrequency (%)
686592892.6 1
< 0.1%
473256847.6 1
< 0.1%
50781069.48 1
< 0.1%
50000763.53 1
< 0.1%
47113884.23 1
< 0.1%
46900376 1
< 0.1%
43494059.78 1
< 0.1%
33598138.54 1
< 0.1%
28894633.59 1
< 0.1%
26700938.59 1
< 0.1%

mbservice
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
False
10634 
True
5749 
ValueCountFrequency (%)
False 10634
64.9%
True 5749
35.1%
2025-01-21T12:19:12.333958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ibservice
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
False
15215 
True
 
1168
ValueCountFrequency (%)
False 15215
92.9%
True 1168
 
7.1%
2025-01-21T12:19:12.352184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

acservice
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
False
11122 
True
5261 
ValueCountFrequency (%)
False 11122
67.9%
True 5261
32.1%
2025-01-21T12:19:12.368306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

kyc
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
True
15188 
False
 
1195
ValueCountFrequency (%)
True 15188
92.7%
False 1195
 
7.3%
2025-01-21T12:19:12.385407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

inactive
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
False
10248 
True
6135 
ValueCountFrequency (%)
False 10248
62.6%
True 6135
37.4%
2025-01-21T12:19:12.401547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct13209
Distinct (%)81.1%
Missing103
Missing (%)0.6%
Memory size128.1 KiB
2025-01-21T12:19:12.514392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.4398649
Min length1

Characters and Unicode

Total characters153681
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11316 ?
Unique (%)69.5%

Sample

1st row9848627268
2nd row9841464720
3rd row9841369614
4th row9856041044
5th row9823452053
ValueCountFrequency (%)
20171022 43
 
0.3%
9.78e+12 35
 
0.2%
20091206 33
 
0.2%
20170908 32
 
0.2%
20170727 32
 
0.2%
20171210 23
 
0.1%
20100412 21
 
0.1%
20171023 20
 
0.1%
20091203 17
 
0.1%
20190428 13
 
0.1%
Other values (13197) 16011
98.3%
2025-01-21T12:19:12.689490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23193
15.1%
9 19663
12.8%
8 19426
12.6%
1 19077
12.4%
2 15438
10.0%
4 15039
9.8%
6 14888
9.7%
5 9381
6.1%
3 8654
 
5.6%
7 8394
 
5.5%
Other values (20) 528
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23193
15.1%
9 19663
12.8%
8 19426
12.6%
1 19077
12.4%
2 15438
10.0%
4 15039
9.8%
6 14888
9.7%
5 9381
6.1%
3 8654
 
5.6%
7 8394
 
5.5%
Other values (20) 528
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23193
15.1%
9 19663
12.8%
8 19426
12.6%
1 19077
12.4%
2 15438
10.0%
4 15039
9.8%
6 14888
9.7%
5 9381
6.1%
3 8654
 
5.6%
7 8394
 
5.5%
Other values (20) 528
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23193
15.1%
9 19663
12.8%
8 19426
12.6%
1 19077
12.4%
2 15438
10.0%
4 15039
9.8%
6 14888
9.7%
5 9381
6.1%
3 8654
 
5.6%
7 8394
 
5.5%
Other values (20) 528
 
0.3%
Distinct4020
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:12.803184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9468962
Min length7

Characters and Unicode

Total characters146577
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1026 ?
Unique (%)6.3%

Sample

1st row01-Jul-18
2nd row13-Sep-17
3rd row08-Jul-10
4th row02-Mar-08
5th row25-Sep-17
ValueCountFrequency (%)
870
 
5.2%
22-oct-17 467
 
2.8%
16-nov-17 443
 
2.6%
08-sep-17 430
 
2.6%
27-jul-17 243
 
1.4%
23-oct-17 222
 
1.3%
10-dec-17 162
 
1.0%
12-nov-17 139
 
0.8%
03-aug-12 93
 
0.6%
06-dec-09 64
 
0.4%
Other values (4010) 13685
81.4%
2025-01-21T12:19:12.954038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 32766
22.4%
1 19936
13.6%
0 10902
 
7.4%
2 8739
 
6.0%
7 5438
 
3.7%
e 4131
 
2.8%
8 3965
 
2.7%
J 3734
 
2.5%
a 3634
 
2.5%
u 3612
 
2.5%
Other values (24) 49720
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 146577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 32766
22.4%
1 19936
13.6%
0 10902
 
7.4%
2 8739
 
6.0%
7 5438
 
3.7%
e 4131
 
2.8%
8 3965
 
2.7%
J 3734
 
2.5%
a 3634
 
2.5%
u 3612
 
2.5%
Other values (24) 49720
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 146577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 32766
22.4%
1 19936
13.6%
0 10902
 
7.4%
2 8739
 
6.0%
7 5438
 
3.7%
e 4131
 
2.8%
8 3965
 
2.7%
J 3734
 
2.5%
a 3634
 
2.5%
u 3612
 
2.5%
Other values (24) 49720
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 146577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 32766
22.4%
1 19936
13.6%
0 10902
 
7.4%
2 8739
 
6.0%
7 5438
 
3.7%
e 4131
 
2.8%
8 3965
 
2.7%
J 3734
 
2.5%
a 3634
 
2.5%
u 3612
 
2.5%
Other values (24) 49720
33.9%
Distinct3781
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:13.054718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9106391
Min length7

Characters and Unicode

Total characters145983
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1924 ?
Unique (%)11.7%

Sample

1st row05-Jan-25
2nd row10-Jan-25
3rd row08-Jul-10
4th row24-Apr-19
5th row03-Jan-25
ValueCountFrequency (%)
1464
 
8.6%
02-jan-25 384
 
2.2%
28-apr-19 311
 
1.8%
11-jan-25 293
 
1.7%
03-jan-25 205
 
1.2%
26-apr-19 204
 
1.2%
10-jan-25 190
 
1.1%
24-dec-17 177
 
1.0%
29-apr-19 176
 
1.0%
04-jan-25 151
 
0.9%
Other values (3771) 13560
79.2%
2025-01-21T12:19:13.199358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 32766
22.4%
1 19532
13.4%
2 9453
 
6.5%
0 7490
 
5.1%
9 5984
 
4.1%
a 5118
 
3.5%
r 4568
 
3.1%
J 4504
 
3.1%
8 4358
 
3.0%
5 4291
 
2.9%
Other values (24) 47919
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 145983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 32766
22.4%
1 19532
13.4%
2 9453
 
6.5%
0 7490
 
5.1%
9 5984
 
4.1%
a 5118
 
3.5%
r 4568
 
3.1%
J 4504
 
3.1%
8 4358
 
3.0%
5 4291
 
2.9%
Other values (24) 47919
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 145983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 32766
22.4%
1 19532
13.4%
2 9453
 
6.5%
0 7490
 
5.1%
9 5984
 
4.1%
a 5118
 
3.5%
r 4568
 
3.1%
J 4504
 
3.1%
8 4358
 
3.0%
5 4291
 
2.9%
Other values (24) 47919
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 145983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 32766
22.4%
1 19532
13.4%
2 9453
 
6.5%
0 7490
 
5.1%
9 5984
 
4.1%
a 5118
 
3.5%
r 4568
 
3.1%
J 4504
 
3.1%
8 4358
 
3.0%
5 4291
 
2.9%
Other values (24) 47919
32.8%
Distinct5285
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:13.296693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.709577
Min length7

Characters and Unicode

Total characters142689
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3562 ?
Unique (%)21.7%

Sample

1st row02-Jan-25
2nd row16-Apr-19
3rd row08-Jul-10
4th row11-Jan-25
5th row05-Jan-25
ValueCountFrequency (%)
4758
 
25.4%
02-jan-25 675
 
3.6%
06-jan-25 240
 
1.3%
11-jan-25 211
 
1.1%
10-jan-25 199
 
1.1%
28-apr-19 151
 
0.8%
04-jan-25 144
 
0.8%
09-jan-25 143
 
0.8%
05-jan-25 127
 
0.7%
26-dec-24 120
 
0.6%
Other values (5275) 11994
63.9%
2025-01-21T12:19:13.435979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 32766
23.0%
1 15020
 
10.5%
11895
 
8.3%
2 9168
 
6.4%
0 7424
 
5.2%
a 5206
 
3.6%
5 4993
 
3.5%
9 4624
 
3.2%
J 4487
 
3.1%
r 3860
 
2.7%
Other values (24) 43246
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 142689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 32766
23.0%
1 15020
 
10.5%
11895
 
8.3%
2 9168
 
6.4%
0 7424
 
5.2%
a 5206
 
3.6%
5 4993
 
3.5%
9 4624
 
3.2%
J 4487
 
3.1%
r 3860
 
2.7%
Other values (24) 43246
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 142689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 32766
23.0%
1 15020
 
10.5%
11895
 
8.3%
2 9168
 
6.4%
0 7424
 
5.2%
a 5206
 
3.6%
5 4993
 
3.5%
9 4624
 
3.2%
J 4487
 
3.1%
r 3860
 
2.7%
Other values (24) 43246
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 142689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 32766
23.0%
1 15020
 
10.5%
11895
 
8.3%
2 9168
 
6.4%
0 7424
 
5.2%
a 5206
 
3.6%
5 4993
 
3.5%
9 4624
 
3.2%
J 4487
 
3.1%
r 3860
 
2.7%
Other values (24) 43246
30.3%

dob
Text

Distinct6282
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
2025-01-21T12:19:13.540086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.087835
Min length7

Characters and Unicode

Total characters132503
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4422 ?
Unique (%)27.0%

Sample

1st row03-Apr-52
2nd row14-Jul-42
3rd row15-Sep-35
4th row29-Jan-36
5th row12-Nov-50
ValueCountFrequency (%)
14944
62.6%
01-jan-45 9
 
< 0.1%
15-jun-38 7
 
< 0.1%
01-jan-16 7
 
< 0.1%
15-aug-36 7
 
< 0.1%
01-jan-14 7
 
< 0.1%
15-aug-38 6
 
< 0.1%
28-apr-44 6
 
< 0.1%
01-jan-49 6
 
< 0.1%
10-oct-49 6
 
< 0.1%
Other values (6272) 8850
37.1%
2025-01-21T12:19:13.684067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37360
28.2%
- 32766
24.7%
1 6174
 
4.7%
2 5559
 
4.2%
0 5031
 
3.8%
4 4504
 
3.4%
3 4034
 
3.0%
5 3423
 
2.6%
a 2631
 
2.0%
J 2373
 
1.8%
Other values (24) 28648
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 132503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
37360
28.2%
- 32766
24.7%
1 6174
 
4.7%
2 5559
 
4.2%
0 5031
 
3.8%
4 4504
 
3.4%
3 4034
 
3.0%
5 3423
 
2.6%
a 2631
 
2.0%
J 2373
 
1.8%
Other values (24) 28648
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 132503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
37360
28.2%
- 32766
24.7%
1 6174
 
4.7%
2 5559
 
4.2%
0 5031
 
3.8%
4 4504
 
3.4%
3 4034
 
3.0%
5 3423
 
2.6%
a 2631
 
2.0%
J 2373
 
1.8%
Other values (24) 28648
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 132503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
37360
28.2%
- 32766
24.7%
1 6174
 
4.7%
2 5559
 
4.2%
0 5031
 
3.8%
4 4504
 
3.4%
3 4034
 
3.0%
5 3423
 
2.6%
a 2631
 
2.0%
J 2373
 
1.8%
Other values (24) 28648
21.6%

Interactions

2025-01-21T12:19:10.308962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.488959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.736101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.916034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.109846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.349802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.531087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.770934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.953954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.152389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.388120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.565927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.803944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.990964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.188779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.431482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.659534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.838154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.026756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.227807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.470989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.697787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:09.877923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.071438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-21T12:19:10.269110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-21T12:19:13.800598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
acbalacserviceactypebranchccycustidibserviceinactivekyclcyacbalmbservicenationalresidentsector
acbal1.0000.0000.0910.0240.000-0.1110.0000.0000.0370.9970.0000.0000.0000.133
acservice0.0001.0000.2230.3390.0110.3710.2780.0140.1060.0000.4480.0270.0110.094
actype0.0910.2231.0000.3940.124-0.1060.2180.1320.1660.0900.2560.0870.0800.344
branch0.0240.3390.3941.0000.541-0.0170.5830.2460.0890.0260.3820.6800.2640.600
ccy0.0000.0110.1240.5411.0000.0440.0000.0450.2030.2080.0470.5720.5590.622
custid-0.1110.371-0.106-0.0170.0441.0000.4020.2620.198-0.1090.3190.0090.0180.359
ibservice0.0000.2780.2180.5830.0000.4021.0000.1960.0600.0000.3170.0000.0000.030
inactive0.0000.0140.1320.2460.0450.2620.1961.0000.1780.0000.1620.0280.0300.085
kyc0.0370.1060.1660.0890.2030.1980.0600.1781.0000.0660.0400.1600.1540.336
lcyacbal0.9970.0000.0900.0260.208-0.1090.0000.0000.0661.0000.0000.2570.2750.158
mbservice0.0000.4480.2560.3820.0470.3190.3170.1620.0400.0001.0000.0180.0280.124
national0.0000.0270.0870.6800.5720.0090.0000.0280.1600.2570.0181.0000.7910.631
resident0.0000.0110.0800.2640.5590.0180.0000.0300.1540.2750.0280.7911.0000.676
sector0.1330.0940.3440.6000.6220.3590.0300.0850.3360.1580.1240.6310.6761.000

Missing values

2025-01-21T12:19:10.545134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-21T12:19:10.630979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

custidnamenationalresidentindustrysectorbranchactypeaccountnoccycategoryacballcyacbalmbserviceibserviceacservicekycinactivemobilenoopendateldrdatelcrdatedob
0444500DEEPAK DHAMINPNP90091500121012104445004NPR6010142204.69142204.69TrueTrueTrueTrueFalse984862726801-Jul-1805-Jan-2502-Jan-2503-Apr-52
1100199UPENDRA P. SUBEDINPNP90091500118011801001998NPR1018-28299.21-28299.21TrueTrueTrueTrueFalse984146472013-Sep-1710-Jan-2516-Apr-1914-Jul-42
267038SIDDHI BHANDARINPNP90091500199019900670380NPR40040.000.00TrueTrueFalseTrueTrue984136961408-Jul-1008-Jul-1008-Jul-1015-Sep-35
367441SHOBHA SHRESTHANPNP90091500121012100674414NPR601018971.8818971.88TrueFalseFalseTrueFalse985604104402-Mar-0824-Apr-1911-Jan-2529-Jan-36
4444277APURVA RAUNIYARNPNP90091500121012104442772NPR601026203.0326203.03TrueTrueTrueTrueFalse982345205325-Sep-1703-Jan-2505-Jan-2512-Nov-50
5403653DIBYA RAJ POKHARELNPNP90091500121012104036537NPR60102817.882817.88TrueFalseTrueTrueFalse984207260703-Aug-1202-Jan-2528-Apr-1928-Sep-37
6164201BIKRAM PRAJAPATINPNP90091500118011801642016NPR1018-300000.00-300000.00TrueFalseTrueTrueFalse984174544617-Oct-1719-Dec-1801-Feb-1805-Oct-45
7444370BISHAL KHANALNPNP90091500121012104443702NPR601028696.8528696.85TrueTrueTrueTrueFalse984521217214-Feb-1825-Apr-1925-Mar-1911-Nov-46
8683010SAROJ JAIRUNPNP90091500121012106830108NPR6010-4485.82-4485.82TrueTrueTrueTrueFalse981634909808-Jul-1810-Jan-2526-Dec-2417-Jul-49
9683204GIRWAN BIKRAM THAPANPNP90091500121012106832045NPR6010-6999.58-6999.58TrueTrueTrueTrueFalse980200505005-Mar-1908-Jan-2525-Apr-1907-May-49
custidnamenationalresidentindustrysectorbranchactypeaccountnoccycategoryacballcyacbalmbserviceibserviceacservicekycinactivemobilenoopendateldrdatelcrdatedob
16373152857SHIVA LAL DARAINPNP900815001222122201528574NPR6007507248.92507248.92FalseFalseFalseTrueFalse980580085518-Nov-0921-Feb-1902-Dec-1813-Aug-17
1637463958MAHESH GAUTAMNPNP900815001240124000639589NPR60238541.058541.05FalseFalseFalseTrueFalse2008090710-Sep-0901-Sep-0916-Jan-40- -
16375242543BHAGAWATI THAPA CHHETRINPNP900815001223122302425435NPR6022407.10407.10FalseFalseTrueFalseTrue984613139105-Mar-1205-Mar-1215-Feb-39- -
16376242627AMBI MAYA THAPANPNP900815001223122302426272NPR6022105923.87105923.87FalseFalseTrueTrueFalse984649745525-Mar-1210-Mar-1928-Apr-1904-Oct-45
16377465390ASMITA THAPA MAGARNPNP900815001236123604653900NPR60195821.035821.03FalseFalseFalseTrueTrue980886855306-Mar-1624-Oct-1626-Sep-1623-Feb-52
16378302556GYAN BAHADUR B.K.NPNP900815001222122203025569NPR6007180.99180.99TrueFalseFalseTrueTrue981836677905-Feb-1412-Mar-1829-Feb-1614-Nov-43
16379182742DOL RAJ REGMINPNP900815001236123601827422NPR60191570.051570.05TrueFalseTrueTrueFalse985608084420-Aug-1007-Apr-1912-Mar-1901-Nov-24
16380182566MOTISARA KUMALNPNP900815001223122301825665NPR6022873.69873.69FalseFalseFalseTrueFalse2010072803-Aug-1106-Sep-1013-May-40- -
16381301354SHREE NARAYAN DATTA SIGDELNPNP900815001246124603013545NPR6024352.73352.73TrueFalseFalseTrueTrue986023351930-Apr-1309-Feb-1711-Sep-1618-Dec-19
16382357989UMESH KUMAR MANDALNPNP900815001236123603579899NPR6019100.06100.06TrueFalseTrueTrueFalse984440823605-Feb-1521-Apr-1921-Apr-1915-Nov-47

Duplicate rows

Most frequently occurring

custidnamenationalresidentindustrysectorbranchactypeaccountnoccycategoryacballcyacbalmbserviceibserviceacservicekycinactivemobilenoopendateldrdatelcrdatedob# duplicates
0284NEPAL RASTRA BANKNPNP40003000145014500002842USD50010.00.0FalseFalseTrueFalseFalseNaN- -- -- -- -2